Human Foot Placement and Balance in the Sagittal Plane
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Foot placement has long been recognized as the primary mechanism that humans use to restore balance. Many biomechanists have examined where humans place their feet during gait, perturbations, and athletic events. Roboticists have also used foot placement as a means of control but with limited success. Recently, Wight et al. (2008, "Introduction of the Foot Placement Estimator: A Dynamic Measure of Balance for Bipedal Robotics," ASME J. Comput. Nonlinear Dyn., 3, p. 011009) introduced a planar foot placement estimator (FPE) algorithm that will restore balance to a simplified biped that is falling. This study tested the FPE as a candidate function for sagittal plane human-foot-placement (HFP) by recording the kinematics of 14 healthy subjects while they performed ten walking trials at three speeds. The FPE was highly correlated with HFP (rho>or=0.997) and its accuracy varied linearly from 2.6 cm to -8.3 cm as walking speed increased. A sensitivity analysis revealed that assumption violations of the FPE cannot account for the velocity-dependent changes in FPE-HFP error suggesting that this behavior is volitional.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it